Unexpected results of SOM learning and its detection
| Autor: | Tsutomu Miyoshi, Yasuto Nishii |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Computer Science::Machine Learning
Self-organizing map Artificial neural network Computer science business.industry Competitive learning Pattern recognition Machine learning computer.software_genre Set (abstract data type) Hybrid Kohonen self-organizing map Feature (computer vision) Unsupervised learning Artificial intelligence business computer Feature learning |
| Zdroj: | SMC |
| DOI: | 10.1109/icsmc.2010.5642344 |
| Popis: | Kohonen's Self Organizing Map (SOM) involves neural networks, for which an algorithm learns the feature of input data through unsupervised, competitive neighborhood learning. In many cases of SOM learning, if the data make classes in input data space with similar density, similar shape, and similar size, corresponding classes in feature map also formed to similar shape and similar size. In the experiments, however, we found unexpected learning results, corresponding classes in feature map formed to different shape and different size one another. In this paper, we investigate what kind of learning data set, which feature of learning data causes unexpected results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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